I. Introduction
There have been issues with shooting human pictures at both official and casual occasions, despite the tremendous advancements in mobile and camera technology in the last several years. These images will be essential for future usage, however fuzzy images could be the consequence of camera shaking [1]. With the increasing need for high-quality visual material across several industries, improving face characteristics in damaged photographs has become a top priority in the field of image restoration. Everyone now expects digital media, from social networks to business apps, to include aesthetically pleasing and finely detailed depictions of faces. Consequently, academics and practitioners are focusing on ways to improve visual communication by tackling picture deterioration. This degradation might be caused by compression artefacts, noise interference, or low-resolution capture. When shooting in low light, the sun's very high exposure value might cause the camera's sensor to overexpose or create noisy photographs. Image restoration technology [2] can fix these issues. Portraits of people, and more specifically their faces, are the major subject of this study report. To restore a blind person's face, a Generative Adversarial Network (GAN) is used, which makes advantage of the diverse and rich priors of the pretrained network. This work used the Generative Facial Prior (GFP) approach for precise face reconstruction [3]. Repairing low-quality (LQ) regions of faces affected by similar issues including sound, fade compressing artifacts, etc. is the main objective of blind facial recovery. Due to a number of aspects, including intricate artifacts, numerous stances, and facial emotions, applying it to actual events is quite challenging [4]. The bulk of methods that aim to recover accurate facial features use priors that are particular to the face, including facial feature maps. Very LQ input photos are used to generate the priors. In terms of restoration-related texture data, the priors are severely lacking. Alternatively, for the purpose of producing realistic results, reference priors, high-quality (HQ) face photos, or facial dictionaries may be used. However, it is severely lacking in variety, since it is restricted to the face features included in that lexicon. These GANS can generate HQ faces with a great deal more data about the texture, colour, lighting, sharpness, etc. [3]. Implementing such priors for the restoration process is tricky. Even though previous techniques used GAN inversion, which provides realistic results, they typically resulted in images with low fidelity.